Our data cleansing tools and the data cleansing techniques we employ, enable us to manipulate your data file to help improve your overall data quality. This can include modifying the data format or data structure.
Even seemingly minor data format issues can present obstacles when processing your data. Luckily, our data cleansing team usually have an answer, and are happy to provide you with a free data quality assessment.
Regardless of how your data is stored, a large CRM database perhaps, or as an excel file maybe, the structure and data format are decisive factors in your ability to use it to its full potential.
Data scrubbing, data cleansing, whatever you decide to call it produce the same result – better data. However, success depends on using the correct data cleaning tools; otherwise, results are likely to be average at best and could ultimately cost more to correct in the future.
What Types of Data Cleaning Tools?
Data cleansing tools are wide and varied, and dependent on what data cleaning task you need to complete there are some great ones out there.
For instance, Excel data cleansing tools and Salesforce data cleansing tools are really useful for general data cleaning and data manipulation.
If though, you have a large dataset that needs complex processing, say, deduping with hierarchy, then our professional data cleaning tools are a far better option.
Our data cleansing experience
After our data refining processes are complete your database will be far more efficient. For instance, reporting will be significantly more insightful because all of your data will be utilised properly.
Optimised data will also allow you to drill down into each section, and hone your marketing campaigns far more effectively.
Data Cleaning Examples
Below is a brief overview of data format issues that crop up on a regular basis when we cleanse name and address data. A more detailed data cleansing guide will be available soon which will showcase our full range of data cleaning tools.
Table 1 has 5 sample records. Each has a combined name field, addr1-addr5, postcode, and an email address.
Table 1 Data Cleansing Examples - Messy data
|1||Mr John Smith||1 The Street||LEED||W. Yorks||L12345firstname.lastname@example.org|
|2||Mr Peter Pan||2 The Street||Any Addr2||Any Addr3||London||E1 2SAemail@example.com|
|3||Mrs Jane Doe||3 THE STREET||Any Addr3||Mancs||M4 1ESfirstname.lastname@example.org|
|4||Miss Sam Sample||4 The Street||NULL||NULL||London||W11 3DBemail@example.com.|
|5||Peter Pan||2 The St||ANY ADDR2||London||E1 2SA|
The name appearing in a single field isn’t a problem. However, the optimal way for this data to be stored within your database is by splitting it into three separate fields – Title, First name, and Surname.
This gives you another option too. You will have the option to create a custom salutation, so you could address the letter formally, or if you prefer, address them by their first name.
Some records have missing address lines or postcodes and there seems to be little or no symmetry whatsoever.
Information is in the wrong field, with parts of the address incomplete or duplicated, while some records are clearly formatted incorrectly.
Incorrect email addresses also need to be rectified. Fortunately, our email data cleaning tool is perfect for this.
ID 4 shows Miss Sam Sample with ‘NULL’ showing in two fields. Some CRM databases will automatically attribute this ‘NULL’ value to a blank field. Consequently, data can be exported like this and cause problems, in a mail merge address block for example.
Dedupe data options
In the original table ID 2 is a duplicate of ID 5. but which record should be kept? With our dedupe processing options you can choose.
In this simple example ID 2 is the better record as it has more address details. For instance, a title appears before his name and he has an email address. It is a more comprehensive record.
Now lets say, for example, you had multiple data sources and one was your ‘Master’ file, the other data consisting of prospect lists. It would be great if you could choose the record to keep. Good news! with our data cleansing tool we can make it happen.
When we find a duplicate match, priority or hierarchy can be given to your master record and the prospect version removed from your mailing file or flagged for your database, if your prefer.
no more manual data correction
Now lets take a look at how your amended table could look after being run through our data cleaning software.
Table 2 Example Data Cleansed
|Mr||John||Smith||1||Mr John Smith||1 The Street||Any Addr2||Any Addr3||Leeds||West Yorkshire||L12 firstname.lastname@example.org|
|Mr||Peter||Pan||2||Mr Peter Pan||2 The Street||Any Addr2||Any Addr3||London||E1 2SAemail@example.com|
|Mrs||Jane||Doe||3||Mrs Jane Doe||3 The Street||Any Addr2||Any Addr3||Manchester||Gt. Manchester||M4 1ESfirstname.lastname@example.org|
|Miss||Sam||Sample||4||Miss Sam Sample||4 The Street||Any Addr2||Any Addr3|| |
The data has been corrected and tidied up and is far more usable. This is obviously a small snapshot of what can be achieved.
If you need advanced data cleaning tools for your next data project, we can help.
Use one of our contact options below to get in touch.